AIMC Topic: Nevus
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Weakly supervised deep learning image analysis can differentiate melanoma from naevi on haematoxylin and eosin-stained histopathology slides.
Journal of the European Academy of Dermatology and Venereology : JEADV
Aug 31, 2024
BACKGROUND: The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)-based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger...
A hybrid CNN with transfer learning for skin cancer disease detection.
Medical & biological engineering & computing
May 18, 2024
The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the precise classification of skin lesions. However, dermatologists may find it difficult and time-consuming ...
Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection.
The Journal of investigative dermatology
Jan 29, 2024
Melanoma is still a major health problem worldwide. Early diagnosis is the first step toward reducing its mortality, but it remains a challenge even for experienced dermatologists. Although computer-aided systems have been developed to help diagnosis...
Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features.
Computational and mathematical methods in medicine
May 28, 2022
The objective of this study was to explore the image classification and case characteristics of pigmented nevus (PN) diagnosed by dermoscopy under deep learning. 268 patients were included as the research objects and they were randomly divided into o...
Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population.
International journal of environmental research and public health
Mar 24, 2022
(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma i...
Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.
Dermatology (Basel, Switzerland)
Jul 8, 2021
BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuab...
Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.
European journal of cancer (Oxford, England : 1990)
Apr 7, 2021
BACKGROUND: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.
Artificial intelligence in dermatopathology: Diagnosis, education, and research.
Journal of cutaneous pathology
Jan 26, 2021
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...
Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.
European journal of cancer (Oxford, England : 1990)
Jan 16, 2021
BACKGROUND: Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application.
Robustness of convolutional neural networks in recognition of pigmented skin lesions.
European journal of cancer (Oxford, England : 1990)
Jan 7, 2021
BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin c...
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